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ERIC Number: EJ853984
Record Type: Journal
Publication Date: 2009-Oct
Pages: 15
Abstractor: As Provided
Reference Count: 0
ISBN: N/A
ISSN: ISSN-0749-596X
Artificial Language Learning and Feature-Based Generalization
Finley, Sara; Badecker, William
Journal of Memory and Language, v61 n3 p423-437 Oct 2009
Abstract representations such as subsegmental phonological features play such a vital role in explanations of phonological processes that many assume that these representations play an equally prominent role in the learning process. This assumption is tested in three artificial grammar experiments involving a mini language with morpho-phonological alternations based on back vowel harmony. In Experiments 1 and 2, adult participants were trained using positive data from four vowels in a six-vowel inventory: the two remaining vowels appeared at test only. If participants use subsegmental phonological features and natural classes for learning, they should generalize to the novel test segments. Results support a subsegmental feature-based learning strategy that makes use of phonetic information and knowledge of phonological principles. A third experiment (Experiment 3) tests for generalizations to novel suffixes, providing further evidence for the generality of learning. (Contains 3 tables and 4 figures.)
Elsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A